知行编程网知行编程网  2022-06-01 07:00 知行编程网 |  隐藏边栏 |   抢沙发 |  3 
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点上方蓝色“菜鸟学Python”，选“星标”

重磅干货，第一时间送到


干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？

在大家的生活中，贷款是不能避开的话题。而银行在向个人提供贷款时，需要对这个人进行一个全面的评估，防止这个人还不上贷款，从而成为坏账。所以利用机器学习来评估一个人是否能够取得贷款，是一个非常热门的话题。

今天，小编就带领大家实现一个完整的机器学习的项目。来预测一个人究竟能否获得银行的贷款。

我们今天用到的库包含了sklearn、matplotlib、numpy、pandas、seaborn、scipy等库，从机器学习到数据分析到数据可视化的展示，可谓是干货满满。



01

数据初探


首先我们读入数据，并对数据进行一个初步的认知：

干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？

可以看到，数据一共包含了12个特征维度，分别有数据的ID(Loan_ID)，性别(Gender)，是否结婚(Married)等信息，而Loan_Status表示的是label，代表该用户是否贷到款。我们将数据中Credit_History特征由数值型转换为标称型，并删除掉Loan_ID这一列。来看一下贷到款和没贷到款的比例。


干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？

可以看出，贷到款的人数占到总人数的69%。在对整体数据有一个整体的把握之后，接下来我们来对每个特征的重要性进行分析。



02

特征分析


首先我们先从标称型数据开始分析。如下图所示：

干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？

上图中，横坐标是每个标称型的数据类型，纵坐标是我们的Label，也即Loan_Status。从上述的数据分布可以得到下面很多的信息:

  • 左上角中Gender和Loan_Status的关系，可以看出，无论是男性还是女性，大部分的人都得到了贷款。

  • 性别在是否提供贷款的判定中并不是特别重要的特征信息。

  • 在右上角的Married和Loan_Status的关系，可以看出大多数结婚的人都得到了贷款

  • 如果一个人结婚了，那么他有很大的概率得到贷款。所以Married对于Loan_Status是一个好的特征，对于其他的标称型类型同样可以采用这样的方法进行分析。

接下来我们来看一下数值型的特征

干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？

可以看出，当CoapplicantInocme的中位数较低时，Loan_Status 为N，所以 CoapplicantInocme是一个很好的特征信息。


03

数据清洗


对特征进行分析后，接下来就是数据的清洗，包括了缺失值的处理，对于标称型的数据，我们将其按照众数来填充，而对于数值型的数据，采用平均值来填充(也可以采用缺失值的上一个值来填充)，然后将标签进行one_hot来编码，最终处理后的数据如下所示。

干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？




04

模型训练与测试


对于数据进行清洗后，接下来就是模型的训练，对于二分类问题，采用logistic回归、KNN、SVM(SVC)和决策树四种模型来训练，并通过准确率，召回率，F1准确率，分类准确率分数五个评价指标来评价模型。


干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？

对于模型的训练，采用了两种方法，第一种是直接将数据集分为训练集和测试集，其中对于训练集和测试集，其label中的Y和N的比例同数据集中的label比例相同。然后再训练集上训练模型，在测试集上测试模型。下图展示的是模型在训练集上训练后在测试集上测试的结果。


干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？

从评价指标和上图展示可以得出，目前最好的模型是logistic模型，而SVC模型相当于只是存储了数据，并没有对数据进行任何的判断分类，呈现过拟合的状态。


对于模型训练的第二种方法是采用K折交叉验证的训练方法，该方法是将原数据有放回的采样N次，每次将原数据分为训练集和测试集，然后进行模型的训练和测试。然后平均N次的评价结果来对模型进行评估。

干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？

从上图右侧的结果可以看出，此时的SVC同样是呈现过拟合的状态，而K折交叉验证下，决策树的结果要好于logistic回归，在测试集上的准确率达到了80.86%，得到训练好的模型后，我们就可以利用该模型来预测个人的数据，来预测他能否得到银行的贷款。

最后，我们来展示一下K折交叉验证的细节：

干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？

上图可以看到，针对logistic回归模型，K折交叉验证每次都重新划分训练集和测试集，来训练模型，在测试集上进行测试。然后将每次训练后的评价指标结果进行保存。最终将K次(这里是10次)的训练结果进行平均，就得到了每个评价指标的平均结果。


end



以上就是小编带领大家进行的一个简单的机器学习项目练习，在这个基础上，大家还可以继续优化，比如说进行特征的组合选择，来优化特征，同时可以针对模型的参数进行调整来提升模型的性能。

希望本篇对大家面试AI算法岗位，有帮助，欢迎在留言区点评，记得给个三连哦！


需要源码和数据集的可以按照下面的方式获取：

1). 长按下面的二维码

2). 后台输入：贷款


干货！百度面试机器学习，除了泰坦尼克，还有银行贷款？
👆长按上方二维码 2 秒 
回复「贷款」即可获取源码资料


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data-darkmode-original-color-15860613985508="rgb(0, 0, 0)" data-darkmode-bgimage-15860613985508="1" data-style="padding: 18px 15px 20px 10px; color: rgb(86, 146, 214); text-align: center; letter-spacing: 1.5px; background-image: url('https://www.zkxjob.com/wp-content/uploads/2022/05/wxsync-2022-05-a2a8a5e1e58f30392066a170034ee027.png'); background-size: 100% 100%; background-repeat: no-repeat; overflow-wrap: break-word !important;" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(230, 230, 230)" data-darkmode-original-color-15870356070738="rgb(0, 0, 0)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(230, 230, 230)" data-darkmode-original-color-15870356071023="rgb(0, 0, 0)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(230, 230, 230)" data-darkmode-original-color-15882384789136="rgb(0, 0, 0)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(230, 230, 230)" data-darkmode-original-color-15882396318564="rgb(0, 0, 0)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(230, 230, 230)" data-darkmode-original-color-15900529136199="rgb(0, 0, 0)" data-darkmode-bgimage-15900529136199="1" style="padding: 18px 15px 20px 10px;background-size: 100% 100%;background-image: url('https://www.zkxjob.com/wp-content/uploads/2022/05/wxsync-2022-05-a2a8a5e1e58f30392066a170034ee027.png');color: rgb(86, 146, 214);letter-spacing: 1.5px;background-repeat: no-repeat;"><section data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(230, 230, 230)" data-darkmode-original-color-15860613985508="rgb(0, 0, 0)" data-darkmode-bgimage-15860613985508="1" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(230, 230, 230)" data-darkmode-original-color-15870356070738="rgb(0, 0, 0)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(230, 230, 230)" data-darkmode-original-color-15870356071023="rgb(0, 0, 0)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(230, 230, 230)" data-darkmode-original-color-15882384789136="rgb(0, 0, 0)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(230, 230, 230)" data-darkmode-original-color-15882396318564="rgb(0, 0, 0)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(230, 230, 230)" data-darkmode-original-color-15900529136199="rgb(0, 0, 0)" data-darkmode-bgimage-15900529136199="1" style="display: flex;justify-content: center;align-items: center;"><section data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(230, 230, 230)" data-darkmode-original-color-15860613985508="rgb(0, 0, 0)" data-darkmode-bgimage-15860613985508="1" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(230, 230, 230)" data-darkmode-original-color-15870356070738="rgb(0, 0, 0)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(230, 230, 230)" data-darkmode-original-color-15870356071023="rgb(0, 0, 0)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(230, 230, 230)" data-darkmode-original-color-15882384789136="rgb(0, 0, 0)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(230, 230, 230)" data-darkmode-original-color-15882396318564="rgb(0, 0, 0)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(230, 230, 230)" data-darkmode-original-color-15900529136199="rgb(0, 0, 0)" data-darkmode-bgimage-15900529136199="1" style="margin-left: 2px;width: 20px;"></section><section data-brushtype="text" data-darkmode-bgcolor-15860613985508="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15860613985508="rgb(255, 255, 255)" data-darkmode-color-15860613985508="rgb(51, 51, 51)" data-darkmode-original-color-15860613985508="rgb(51, 51, 51)" data-darkmode-bgimage-15860613985508="1" data-darkmode-bgcolor-15870356070738="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356070738="rgb(255, 255, 255)" data-darkmode-color-15870356070738="rgb(51, 51, 51)" data-darkmode-original-color-15870356070738="rgb(51, 51, 51)" data-darkmode-bgimage-15870356070738="1" data-darkmode-bgcolor-15870356071023="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15870356071023="rgb(255, 255, 255)" data-darkmode-color-15870356071023="rgb(51, 51, 51)" data-darkmode-original-color-15870356071023="rgb(51, 51, 51)" data-darkmode-bgimage-15870356071023="1" data-darkmode-bgcolor-15882384789136="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882384789136="rgb(255, 255, 255)" data-darkmode-color-15882384789136="rgb(51, 51, 51)" data-darkmode-original-color-15882384789136="rgb(51, 51, 51)" data-darkmode-bgimage-15882384789136="1" data-darkmode-bgcolor-15882396318564="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15882396318564="rgb(255, 255, 255)" data-darkmode-color-15882396318564="rgb(51, 51, 51)" data-darkmode-original-color-15882396318564="rgb(51, 51, 51)" data-darkmode-bgimage-15882396318564="1" data-darkmode-bgcolor-15900529136199="rgb(36, 36, 36)" data-darkmode-original-bgcolor-15900529136199="rgb(255, 255, 255)" data-darkmode-color-15900529136199="rgb(51, 51, 51)" data-darkmode-original-color-15900529136199="rgb(51, 51, 51)" data-darkmode-bgimage-15900529136199="1" style="font-size: 14px;color: rgb(51, 51, 51);text-align: left;"><span style="font-family: 楷体, 楷体_GB2312, SimKai;">点这里，获取一大波福利</span><span style="font-family: mp-quote, -apple-system-font, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;font-size: 17px;text-align: justify;"></span></section></section></section></section></section></section></section>

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